Detailed Damage Determination with Image Cropping

2. The computer-implemented method of claim 1, further comprising pre-processing the received one or more images of the vehicle to determine which of the one or more images of the vehicle meet a predetermined crop suitability threshold.

3. The computer-implemented method of claim 2, wherein the predetermined crop suitability threshold comprises determining whether the content of each of the one or more images comprises one or more close ups of the vehicle.

4. The computer-implemented method of claim 2, wherein the predetermined crop suitability threshold comprises determining whether the content of each of the one or more images comprises one or more damage areas.

5. The computer-implemented method of claim 2, wherein pre-processing the received one or more images of the vehicle comprises one or more trained preliminary classification models, optionally the one or more trained models comprise convolutional neural networks.

6. The computer-implemented method of claim 1, further comprising determining a plurality of parts of the damaged vehicle that are represented in the plurality of images of the vehicle comprises the use of a plurality of classifiers.

7. The computer-implemented method of claim 6, wherein each one of the plurality of classifiers is operable to detect each of the parts of the vehicle.

8. The computer-implemented method of claim 1, wherein the damage states of parts of the vehicle are determined as one or more quantitative values.

9. The computer-implemented method of claim 1, wherein the image resolution of each of the received plurality of images is maintained.

10. The computer-implemented method of claim 1, wherein the one or more trained models comprise any one or any combination of a neural network; a convolutional neural network; or a recurrent neural network.

11. The computer-implemented method of claim 1, wherein determining the one or more classifications or determining the one or more damage states comprises using a multi-instance learned approach.

12. The computer-implemented method of claim 1, further comprising segmenting each of the plurality of images to produce one or more segmentations and wherein determining the one or more classifications uses the one or more segmentations determined for each of the plurality of images.